From dedd403ec1ab6cbc9af9980ec98a02c574db90bd Mon Sep 17 00:00:00 2001
From: Yeyi12 <sarah.y.gado@gmail.com>
Date: Tue, 11 Mar 2025 23:13:45 +0000
Subject: [PATCH] Addedsome data files and progress on parameters finding

---
 Selected_features.csv          |   11 +
 filtered_selected_features.csv | 2150 ++++++++++++++++++++++++++++++++
 models.ipynb                   |  955 ++++++++++++++
 preprocessing.ipynb            |   49 +-
 4 files changed, 3162 insertions(+), 3 deletions(-)
 create mode 100644 Selected_features.csv
 create mode 100644 filtered_selected_features.csv
 create mode 100644 models.ipynb

diff --git a/Selected_features.csv b/Selected_features.csv
new file mode 100644
index 0000000..4d9ee39
--- /dev/null
+++ b/Selected_features.csv
@@ -0,0 +1,11 @@
+Run,Best Correlation Score,Best Accuracy Score,Selected Features
+Run 1,0.1955,0.9102,"['CardiovascularDisease', 'BehavioralProblems', 'SleepQuality', 'DifficultyCompletingTasks', 'DietQuality', 'PersonalityChanges', 'FamilyHistoryAlzheimers', 'DiastolicBP', 'CholesterolTriglycerides', 'MemoryComplaints', 'FunctionalAssessment', 'ADL', 'CholesterolHDL', 'MMSE', 'AlcoholConsumption']"
+Run 2,0.2241,0.9136,"['Diabetes', 'BehavioralProblems', 'Confusion', 'FamilyHistoryAlzheimers', 'Gender', 'DiastolicBP', 'MemoryComplaints', 'FunctionalAssessment', 'ADL', 'Ethnicity', 'MMSE', 'Hypertension']"
+Run 3,0.248,0.9182,"['BehavioralProblems', 'Gender', 'MemoryComplaints', 'FunctionalAssessment', 'ADL', 'MMSE']"
+Run 4,0.2474,0.9129,"['Diabetes', 'BehavioralProblems', 'DietQuality', 'EducationLevel', 'Gender', 'CholesterolTriglycerides', 'MemoryComplaints', 'CholesterolHDL', 'FunctionalAssessment', 'CholesterolTotal', 'ADL', 'MMSE']"
+Run 5,0.214,0.9122,"['BehavioralProblems', 'FunctionalAssessment', 'ADL', 'CholesterolHDL', 'Confusion', 'CardiovascularDisease', 'SleepQuality', 'HeadInjury', 'DietQuality', 'PersonalityChanges', 'PhysicalActivity', 'Disorientation', 'DiastolicBP', 'Ethnicity', 'Smoking', 'MMSE', 'FamilyHistoryAlzheimers', 'MemoryComplaints', 'AlcoholConsumption']"
+Run 6,0.2195,0.9142,"['Diabetes', 'BehavioralProblems', 'SleepQuality', 'Confusion', 'PersonalityChanges', 'EducationLevel', 'MemoryComplaints', 'FunctionalAssessment', 'ADL', 'Ethnicity', 'MMSE', 'SystolicBP']"
+Run 7,0.2142,0.9209,"['CardiovascularDisease', 'BehavioralProblems', 'SleepQuality', 'Forgetfulness', 'DietQuality', 'PhysicalActivity', 'Smoking', 'Disorientation', 'CholesterolTriglycerides', 'MemoryComplaints', 'FunctionalAssessment', 'ADL', 'MMSE', 'Hypertension']"
+Run 8,0.2152,0.9156,"['BehavioralProblems', 'Disorientation', 'MemoryComplaints', 'FunctionalAssessment', 'ADL', 'MMSE', 'Hypertension']"
+Run 9,0.1906,0.9182,"['Diabetes', 'BehavioralProblems', 'SleepQuality', 'DietQuality', 'Disorientation', 'Gender', 'MemoryComplaints', 'FunctionalAssessment', 'ADL', 'Ethnicity', 'MMSE', 'AlcoholConsumption']"
+Run 10,0.2151,0.9169,"['BehavioralProblems', 'DifficultyCompletingTasks', 'SleepQuality', 'PersonalityChanges', 'DietQuality', 'Confusion', 'Forgetfulness', 'Gender', 'HeadInjury', 'MemoryComplaints', 'CholesterolTriglycerides', 'FunctionalAssessment', 'ADL', 'MMSE']"
diff --git a/filtered_selected_features.csv b/filtered_selected_features.csv
new file mode 100644
index 0000000..e561053
--- /dev/null
+++ b/filtered_selected_features.csv
@@ -0,0 +1,2150 @@
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diff --git a/models.ipynb b/models.ipynb
new file mode 100644
index 0000000..55253b1
--- /dev/null
+++ b/models.ipynb
@@ -0,0 +1,955 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import ast\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.model_selection import RandomizedSearchCV\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import cross_val_score, KFold\n",
+    "from sklearn.svm import SVC\n",
+    "from scipy.stats import randint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Run</th>\n",
+       "      <th>Best Correlation Score</th>\n",
+       "      <th>Best Accuracy Score</th>\n",
+       "      <th>Selected Features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
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+       "      <td>['CardiovascularDisease', 'BehavioralProblems'...</td>\n",
+       "    </tr>\n",
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+       "    <tr>\n",
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+       "      <td>['Diabetes', 'BehavioralProblems', 'DietQualit...</td>\n",
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+       "      <td>['BehavioralProblems', 'FunctionalAssessment',...</td>\n",
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+       "      <td>['Diabetes', 'BehavioralProblems', 'SleepQuali...</td>\n",
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+       "      <td>['BehavioralProblems', 'Disorientation', 'Memo...</td>\n",
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+       "    <tr>\n",
+       "      <th>9</th>\n",
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+       "      <td>['BehavioralProblems', 'DifficultyCompletingTa...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Run  Best Correlation Score  Best Accuracy Score  \\\n",
+       "0   Run 1                  0.1955               0.9102   \n",
+       "1   Run 2                  0.2241               0.9136   \n",
+       "2   Run 3                  0.2480               0.9182   \n",
+       "3   Run 4                  0.2474               0.9129   \n",
+       "4   Run 5                  0.2140               0.9122   \n",
+       "5   Run 6                  0.2195               0.9142   \n",
+       "6   Run 7                  0.2142               0.9209   \n",
+       "7   Run 8                  0.2152               0.9156   \n",
+       "8   Run 9                  0.1906               0.9182   \n",
+       "9  Run 10                  0.2151               0.9169   \n",
+       "\n",
+       "                                   Selected Features  \n",
+       "0  ['CardiovascularDisease', 'BehavioralProblems'...  \n",
+       "1  ['Diabetes', 'BehavioralProblems', 'Confusion'...  \n",
+       "2  ['BehavioralProblems', 'Gender', 'MemoryCompla...  \n",
+       "3  ['Diabetes', 'BehavioralProblems', 'DietQualit...  \n",
+       "4  ['BehavioralProblems', 'FunctionalAssessment',...  \n",
+       "5  ['Diabetes', 'BehavioralProblems', 'SleepQuali...  \n",
+       "6  ['CardiovascularDisease', 'BehavioralProblems'...  \n",
+       "7  ['BehavioralProblems', 'Disorientation', 'Memo...  \n",
+       "8  ['Diabetes', 'BehavioralProblems', 'SleepQuali...  \n",
+       "9  ['BehavioralProblems', 'DifficultyCompletingTa...  "
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('Selected_features.csv')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['BehavioralProblems',\n",
+       " 'Gender',\n",
+       " 'MemoryComplaints',\n",
+       " 'FunctionalAssessment',\n",
+       " 'ADL',\n",
+       " 'MMSE']"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sf = df.loc[df[\"Run\"] == \"Run 3\", \"Selected Features\"].values[0]\n",
+    "sf\n",
+    "sf_list = ast.literal_eval(sf)\n",
+    "sf_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
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+       "      <th></th>\n",
+       "      <th>PatientID</th>\n",
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+       "      <th>EducationLevel</th>\n",
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+       "      <th>...</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2144</th>\n",
+       "      <td>6895</td>\n",
+       "      <td>61</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>39.121757</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.561126</td>\n",
+       "      <td>4.049964</td>\n",
+       "      <td>6.555306</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.492838</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>XXXConfid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2145</th>\n",
+       "      <td>6896</td>\n",
+       "      <td>75</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>17.857903</td>\n",
+       "      <td>0</td>\n",
+       "      <td>18.767261</td>\n",
+       "      <td>1.360667</td>\n",
+       "      <td>2.904662</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.204952</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>XXXConfid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2146</th>\n",
+       "      <td>6897</td>\n",
+       "      <td>77</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15.476479</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.594670</td>\n",
+       "      <td>9.886002</td>\n",
+       "      <td>8.120025</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5.036334</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>XXXConfid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2147</th>\n",
+       "      <td>6898</td>\n",
+       "      <td>78</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15.299911</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.674505</td>\n",
+       "      <td>6.354282</td>\n",
+       "      <td>1.263427</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.785399</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>XXXConfid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2148</th>\n",
+       "      <td>6899</td>\n",
+       "      <td>72</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>33.289738</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.890703</td>\n",
+       "      <td>6.570993</td>\n",
+       "      <td>7.941404</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8.327563</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>XXXConfid</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2149 rows × 35 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      PatientID  Age  Gender  Ethnicity  EducationLevel        BMI  Smoking  \\\n",
+       "0          4751   73       0          0               2  22.927749        0   \n",
+       "1          4752   89       0          0               0  26.827681        0   \n",
+       "2          4753   73       0          3               1  17.795882        0   \n",
+       "3          4754   74       1          0               1  33.800817        1   \n",
+       "4          4755   89       0          0               0  20.716974        0   \n",
+       "...         ...  ...     ...        ...             ...        ...      ...   \n",
+       "2144       6895   61       0          0               1  39.121757        0   \n",
+       "2145       6896   75       0          0               2  17.857903        0   \n",
+       "2146       6897   77       0          0               1  15.476479        0   \n",
+       "2147       6898   78       1          3               1  15.299911        0   \n",
+       "2148       6899   72       0          0               2  33.289738        0   \n",
+       "\n",
+       "      AlcoholConsumption  PhysicalActivity  DietQuality  ...  \\\n",
+       "0              13.297218          6.327112     1.347214  ...   \n",
+       "1               4.542524          7.619885     0.518767  ...   \n",
+       "2              19.555085          7.844988     1.826335  ...   \n",
+       "3              12.209266          8.428001     7.435604  ...   \n",
+       "4              18.454356          6.310461     0.795498  ...   \n",
+       "...                  ...               ...          ...  ...   \n",
+       "2144            1.561126          4.049964     6.555306  ...   \n",
+       "2145           18.767261          1.360667     2.904662  ...   \n",
+       "2146            4.594670          9.886002     8.120025  ...   \n",
+       "2147            8.674505          6.354282     1.263427  ...   \n",
+       "2148            7.890703          6.570993     7.941404  ...   \n",
+       "\n",
+       "      MemoryComplaints  BehavioralProblems       ADL  Confusion  \\\n",
+       "0                    0                   0  1.725883          0   \n",
+       "1                    0                   0  2.592424          0   \n",
+       "2                    0                   0  7.119548          0   \n",
+       "3                    0                   1  6.481226          0   \n",
+       "4                    0                   0  0.014691          0   \n",
+       "...                ...                 ...       ...        ...   \n",
+       "2144                 0                   0  4.492838          1   \n",
+       "2145                 0                   1  9.204952          0   \n",
+       "2146                 0                   0  5.036334          0   \n",
+       "2147                 0                   0  3.785399          0   \n",
+       "2148                 0                   1  8.327563          0   \n",
+       "\n",
+       "      Disorientation  PersonalityChanges  DifficultyCompletingTasks  \\\n",
+       "0                  0                   0                          1   \n",
+       "1                  0                   0                          0   \n",
+       "2                  1                   0                          1   \n",
+       "3                  0                   0                          0   \n",
+       "4                  0                   1                          1   \n",
+       "...              ...                 ...                        ...   \n",
+       "2144               0                   0                          0   \n",
+       "2145               0                   0                          0   \n",
+       "2146               0                   0                          0   \n",
+       "2147               0                   0                          0   \n",
+       "2148               1                   0                          0   \n",
+       "\n",
+       "      Forgetfulness  Diagnosis  DoctorInCharge  \n",
+       "0                 0          0       XXXConfid  \n",
+       "1                 1          0       XXXConfid  \n",
+       "2                 0          0       XXXConfid  \n",
+       "3                 0          0       XXXConfid  \n",
+       "4                 0          0       XXXConfid  \n",
+       "...             ...        ...             ...  \n",
+       "2144              0          1       XXXConfid  \n",
+       "2145              0          1       XXXConfid  \n",
+       "2146              0          1       XXXConfid  \n",
+       "2147              1          1       XXXConfid  \n",
+       "2148              1          0       XXXConfid  \n",
+       "\n",
+       "[2149 rows x 35 columns]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "main_df = pd.read_csv('alzheimers_disease_data.csv')\n",
+    "main_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>BehavioralProblems</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>MemoryComplaints</th>\n",
+       "      <th>FunctionalAssessment</th>\n",
+       "      <th>ADL</th>\n",
+       "      <th>MMSE</th>\n",
+       "      <th>Diagnosis</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>6.518877</td>\n",
+       "      <td>1.725883</td>\n",
+       "      <td>21.463532</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
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+       "      <td>0</td>\n",
+       "      <td>7.118696</td>\n",
+       "      <td>2.592424</td>\n",
+       "      <td>20.613267</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5.895077</td>\n",
+       "      <td>7.119548</td>\n",
+       "      <td>7.356249</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.965106</td>\n",
+       "      <td>6.481226</td>\n",
+       "      <td>13.991127</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.045039</td>\n",
+       "      <td>0.014691</td>\n",
+       "      <td>13.517609</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2144</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.238667</td>\n",
+       "      <td>4.492838</td>\n",
+       "      <td>1.201190</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2145</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.687480</td>\n",
+       "      <td>9.204952</td>\n",
+       "      <td>6.458060</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2146</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.972137</td>\n",
+       "      <td>5.036334</td>\n",
+       "      <td>17.011003</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2147</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5.173891</td>\n",
+       "      <td>3.785399</td>\n",
+       "      <td>4.030491</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2148</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.307543</td>\n",
+       "      <td>8.327563</td>\n",
+       "      <td>11.114777</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2149 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      BehavioralProblems  Gender  MemoryComplaints  FunctionalAssessment  \\\n",
+       "0                      0       0                 0              6.518877   \n",
+       "1                      0       0                 0              7.118696   \n",
+       "2                      0       0                 0              5.895077   \n",
+       "3                      1       1                 0              8.965106   \n",
+       "4                      0       0                 0              6.045039   \n",
+       "...                  ...     ...               ...                   ...   \n",
+       "2144                   0       0                 0              0.238667   \n",
+       "2145                   1       0                 0              8.687480   \n",
+       "2146                   0       0                 0              1.972137   \n",
+       "2147                   0       1                 0              5.173891   \n",
+       "2148                   1       0                 0              6.307543   \n",
+       "\n",
+       "           ADL       MMSE  Diagnosis  \n",
+       "0     1.725883  21.463532          0  \n",
+       "1     2.592424  20.613267          0  \n",
+       "2     7.119548   7.356249          0  \n",
+       "3     6.481226  13.991127          0  \n",
+       "4     0.014691  13.517609          0  \n",
+       "...        ...        ...        ...  \n",
+       "2144  4.492838   1.201190          1  \n",
+       "2145  9.204952   6.458060          1  \n",
+       "2146  5.036334  17.011003          1  \n",
+       "2147  3.785399   4.030491          1  \n",
+       "2148  8.327563  11.114777          0  \n",
+       "\n",
+       "[2149 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_filtered = main_df[sf_list + ['Diagnosis']]\n",
+    "df_filtered.to_csv(\"filtered_selected_features.csv\", index=False)\n",
+    "df_filtered\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                      BehavioralProblems    Gender  MemoryComplaints  \\\n",
+      "BehavioralProblems              1.000000  0.006099         -0.009765   \n",
+      "Gender                          0.006099  1.000000          0.003880   \n",
+      "MemoryComplaints               -0.009765  0.003880          1.000000   \n",
+      "FunctionalAssessment           -0.021941  0.033324          0.002320   \n",
+      "ADL                             0.043376  0.003865         -0.037511   \n",
+      "MMSE                            0.025408  0.025330          0.007652   \n",
+      "Diagnosis                       0.224350 -0.020975          0.306742   \n",
+      "\n",
+      "                      FunctionalAssessment       ADL      MMSE  Diagnosis  \n",
+      "BehavioralProblems               -0.021941  0.043376  0.025408   0.224350  \n",
+      "Gender                            0.033324  0.003865  0.025330  -0.020975  \n",
+      "MemoryComplaints                  0.002320 -0.037511  0.007652   0.306742  \n",
+      "FunctionalAssessment              1.000000  0.053904  0.024932  -0.364898  \n",
+      "ADL                               0.053904  1.000000  0.003359  -0.332346  \n",
+      "MMSE                              0.024932  0.003359  1.000000  -0.237126  \n",
+      "Diagnosis                        -0.364898 -0.332346 -0.237126   1.000000  \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "correlation_matrix = df_filtered.corr()\n",
+    "print(correlation_matrix)\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\", fmt=\".2f\", linewidths=0.5)\n",
+    "plt.title(\"Correlation Matrix\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Pre-processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df_filtered.drop('Diagnosis', axis= 1)\n",
+    "y = df_filtered['Diagnosis']\n",
+    "\n",
+    "#split the data into test and train\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "#apply scaler\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Building the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kf = KFold(n_splits= 5, shuffle= True, random_state= 42)\n",
+    "\n",
+    "models = {\n",
+    "    'DecisionTree': DecisionTreeClassifier(), 'RandomForest': RandomForestClassifier(), 'svc': SVC()\n",
+    "}\n",
+    "\n",
+    "# Define parameters to test using the randomized grid search\n",
+    "param_grids = {\n",
+    "    'DecisionTree': {\n",
+    "        'criterion': ['gini', 'entropy'],\n",
+    "        'max_depth': [None, 10, 20, 30, 50],\n",
+    "        'min_samples_split': randint(2, 10),\n",
+    "        'min_samples_leaf': randint(1, 5)\n",
+    "    },\n",
+    "    \n",
+    "    'RandomForest': {\n",
+    "        'n_estimators': randint(50, 200),\n",
+    "        'criterion': ['gini', 'entropy'],\n",
+    "        'max_depth': [None, 10, 20, 30, 50],\n",
+    "        'min_samples_split': randint(2, 10),\n",
+    "        'min_samples_leaf': randint(1, 5)\n",
+    "    },\n",
+    "    \n",
+    "    'svc': {\n",
+    "        'C': [0.1, 1, 10, 100, 1000],\n",
+    "        'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n",
+    "        'gamma': ['scale', 'auto', 0.001, 0.01, 0.1, 1, 10]\n",
+    "    }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running RandomizedSearchCV for DecisionTree...\n",
+      "Best parameters for DecisionTree: {'criterion': 'entropy', 'max_depth': 10, 'min_samples_leaf': 4, 'min_samples_split': 7}\n",
+      "\n",
+      "Running RandomizedSearchCV for RandomForest...\n",
+      "Best parameters for RandomForest: {'criterion': 'entropy', 'max_depth': 10, 'min_samples_leaf': 4, 'min_samples_split': 7, 'n_estimators': 195}\n",
+      "\n",
+      "Running RandomizedSearchCV for svc...\n"
+     ]
+    }
+   ],
+   "source": [
+    "for name, model in models.items():\n",
+    "    #print(name)\n",
+    "    print(f\"Running RandomizedSearchCV for {name}...\")\n",
+    "    random_search = RandomizedSearchCV(model, param_distributions=param_grids[name], cv =kf, n_iter =100, random_state=42, n_jobs=-1)\n",
+    "    random_search.fit(X_train_scaled, y_train)\n",
+    "    print(f\"Best parameters for {name}: {random_search.best_params_}\\n\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/preprocessing.ipynb b/preprocessing.ipynb
index acea9b2..6e521fa 100644
--- a/preprocessing.ipynb
+++ b/preprocessing.ipynb
@@ -2,9 +2,52 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['c:\\\\Users\\\\sarah\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python313\\\\python313.zip', 'c:\\\\Users\\\\sarah\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python313\\\\DLLs', 'c:\\\\Users\\\\sarah\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python313\\\\Lib', 'c:\\\\Users\\\\sarah\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python313', '', 'C:\\\\Users\\\\sarah\\\\AppData\\\\Roaming\\\\Python\\\\Python313\\\\site-packages', 'C:\\\\Users\\\\sarah\\\\AppData\\\\Roaming\\\\Python\\\\Python313\\\\site-packages\\\\win32', 'C:\\\\Users\\\\sarah\\\\AppData\\\\Roaming\\\\Python\\\\Python313\\\\site-packages\\\\win32\\\\lib', 'C:\\\\Users\\\\sarah\\\\AppData\\\\Roaming\\\\Python\\\\Python313\\\\site-packages\\\\Pythonwin', 'c:\\\\Users\\\\sarah\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python313\\\\Lib\\\\site-packages']\n"
+     ]
+    },
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pandas'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msys\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28mprint\u001b[39m(sys.path)  \u001b[38;5;66;03m# Check where Python is looking for packages\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[38;5;28mprint\u001b[39m(pd.__version__)  \u001b[38;5;66;03m# Should print the installed pandas version\u001b[39;00m\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'pandas'"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "print(sys.path)  # Check where Python is looking for packages\n",
+    "import pandas as pd\n",
+    "print(pd.__version__)  # Should print the installed pandas version\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'pandas'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'pandas'"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -2819,7 +2862,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.13.2"
   }
  },
  "nbformat": 4,
-- 
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